In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
img = mpimg.imread('camera_cal/calibration1.jpg')
plt.imshow(img)
Out[1]:
In [2]:
objpoints = []
imgpoints = []
objp = np.zeros((6*9, 3), np.float32)
objp[:,:2] = np.mgrid[0:9:, 0:6].T.reshape(-1,2)
images = glob.glob('camera_cal/calibration*.jpg')
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = mpimg.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (9,6), corners, ret)
write_name = 'corners_found'+str(idx)+'.jpg'
cv2.imwrite(write_name, img)
In [3]:
import pickle
# Test undistortion on an image
def undistort_one_img(input_dir, file_name, output_dir, show_res):
name = file_name.split('.')[0]
img = mpimg.imread(input_dir + '/' + file_name)
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given
# object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
dstRGB = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
cv2.imwrite(output_dir + '/' + name + '_undistorted.jpg', dstRGB)
# Save the camera calibration result
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump(dist_pickle, open(output_dir + '/' + name + '_undistorted_pickle.p', "wb"))
# Visualize undistortion
if show_res == True:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)
In [4]:
# Undistort an example chessboard image
undistort_one_img('camera_cal', 'calibration2.jpg', 'camera_cal', True)
In [5]:
# Undistort all the input images
undistort_one_img('test_images', 'test1.jpg', 'output_images', True)
undistort_one_img('test_images', 'test2.jpg', 'output_images', False)
undistort_one_img('test_images', 'test3.jpg', 'output_images', False)
undistort_one_img('test_images', 'test4.jpg', 'output_images', False)
undistort_one_img('test_images', 'test5.jpg', 'output_images', False)
undistort_one_img('test_images', 'test6.jpg', 'output_images', False)
In [6]:
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh_min=0, thresh_max=255):
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the derivative in x or y given orient = 'x' or 'y'
if orient == 'x':
sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
if orient == 'y':
sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
binary = np.zeros_like(scaled_sobel)
# 6) Return this mask as your binary_output image
binary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
return binary
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# Return the binary image
return binary_output
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# Return the binary image
return binary_output
def color_thresh(image):
hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
# Threshold color channel
s_thresh_min = 100
s_thresh_max = 255
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh_min) & (s_channel <= s_thresh_max)] = 1
return s_binary
def white_thresh(image):
r_channel = image[:,:,2]
g_channel = image[:,:,1]
b_channel = image[:,:,0]
thresh_min = 120
res_binary = np.zeros_like(r_channel)
res_binary[(r_channel >= thresh_min) & (g_channel >= thresh_min) & (b_channel >= thresh_min)] = 1
return res_binary
def gray_thresh(image):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
g_binary = np.zeros_like(gray)
g_binary[gray >= 100] = 1
return g_binary
def get_binary_img(img):
# Sobel
ksize = 11
gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=ksize, thresh_min=40, thresh_max=100)
grady = abs_sobel_thresh(img, orient='y', sobel_kernel=ksize, thresh_min=40, thresh_max=100)
mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=(50, 100))
dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(0.7, 1.3))
sxbinary = np.zeros_like(dir_binary)
sxbinary[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
# Color
s_binary = color_thresh(img)
# Combine the two binary thresholds
combined_binary = np.zeros_like(sxbinary)
combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
return combined_binary
def get_binary(input_dir, file_name, output_dir, show_res):
name = file_name.split('.')[0]
img = mpimg.imread(input_dir + '/' + file_name)
combined_binary = get_binary_img(img)
cv2.imwrite(output_dir + '/' + name + '_binary.jpg', combined_binary)
if show_res == True:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Undistorted Image', fontsize=30)
ax2.imshow(combined_binary, cmap='gray')
ax2.set_title('Binary Image', fontsize=30)
get_binary('output_images', 'test1_undistorted.jpg', 'output_images', True)
get_binary('output_images', 'test2_undistorted.jpg', 'output_images', False)
get_binary('output_images', 'test3_undistorted.jpg', 'output_images', False)
get_binary('output_images', 'test4_undistorted.jpg', 'output_images', False)
get_binary('output_images', 'test5_undistorted.jpg', 'output_images', False)
get_binary('output_images', 'test6_undistorted.jpg', 'output_images', False)
In [7]:
img = mpimg.imread('test_images/test1.jpg')
plt.imshow(img)
Out[7]:
In [8]:
def get_transform_M(img):
src = np.float32(
[[434, 575],
[910, 575],
[348, 637],
[1029, 637]])
dst = np.float32(
[[391, 555],
[940, 555],
[391, 647],
[940, 647]])
M = cv2.getPerspectiveTransform(src, dst)
inv_M = cv2.getPerspectiveTransform(dst, src)
return (M, inv_M)
def warp(img):
img_size = (img.shape[1], img.shape[0])
M, inv_M = get_transform_M(img)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
return warped
def show_warped(name, show_res):
img = mpimg.imread(name)
warp_img = warp(img)
if show_res == True:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Undistorted Image', fontsize=30)
ax2.imshow(warp_img)
ax2.set_title('Warped Image', fontsize=30)
return warp_img
def generate_binary(img, show_res):
ksize = 7
gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=ksize, thresh_min=50, thresh_max=100)
#grady = abs_sobel_thresh(img, orient='y', sobel_kernel=ksize, thresh_min=20, thresh_max=100)
#mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=(30, 100))
dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(0.7, 1.3))
sxbinary = np.zeros_like(gradx)
sxbinary[(gradx == 1) & (dir_binary == 1)] = 1
#sxbinary[((mag_binary == 1) & (dir_binary == 1))] = 1
#sxbinary[gradx == 1] = 1
while_binary = white_thresh(img)
s_binary = color_thresh(img)
g_binary = gray_thresh(img)
combined_binary = np.zeros_like(sxbinary)
#combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
combined_binary[(((s_binary == 1) | (sxbinary == 1)) & g_binary == 1) | ((while_binary == 1) & (gradx == 1))] = 1
if show_res == True:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Warped Image', fontsize=30)
ax2.imshow(combined_binary, cmap='gray')
ax2.set_title('Binary Warped Image', fontsize=30)
return combined_binary
In [9]:
warp_imgs = []
warp_imgs.append(show_warped('output_images/test1_undistorted.jpg', True))
In [10]:
warp_imgs.append(show_warped('output_images/test2_undistorted.jpg', True))
In [11]:
warp_imgs.append(show_warped('output_images/test3_undistorted.jpg', True))
In [12]:
warp_imgs.append(show_warped('output_images/test4_undistorted.jpg', True))
In [13]:
warp_imgs.append(show_warped('output_images/test5_undistorted.jpg', True))
In [14]:
warp_imgs.append(show_warped('output_images/test6_undistorted.jpg', True))
In [15]:
warp_binary_imgs = []
warp_binary_imgs.append(generate_binary(warp_imgs[0], True))
In [16]:
warp_binary_imgs.append(generate_binary(warp_imgs[1], True))
In [17]:
warp_binary_imgs.append(generate_binary(warp_imgs[2], True))
In [18]:
warp_binary_imgs.append(generate_binary(warp_imgs[3], True))
In [19]:
warp_binary_imgs.append(generate_binary(warp_imgs[4], True))
In [20]:
warp_binary_imgs.append(generate_binary(warp_imgs[5], True))
In [21]:
def get_curvature(ploty, leftx, rightx):
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
y_eval = np.max(ploty)
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
return (left_curverad, right_curverad)
def fit_lane_line(binary_warped, show_res):
# Assuming you have created a warped binary image called "binary_warped"
# Create an output image to draw on and visualize the result
x = binary_warped.shape[0]
y = binary_warped.shape[1]
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[:,100:y-100], axis=0)
out_img = np.empty((x,y,3))
out_img[:,:,0] = binary_warped
out_img[:,:,1] = binary_warped
out_img[:,:,2] = binary_warped
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint]) + 100
rightx_base = np.argmax(histogram[midpoint:]) + midpoint + 100
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
#out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
#out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
x_curvature, y_curvature = get_curvature(ploty, left_fitx, right_fitx)
if show_res:
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
print('curvature:', x_curvature, 'm', y_curvature, 'm')
return (left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base)
In [22]:
def final_visualize(binary_warped, left_fit, right_fit):
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
x = binary_warped.shape[0]
y = binary_warped.shape[1]
out_img = np.empty((x,y,3))
out_img[:,:,0] = binary_warped
out_img[:,:,1] = binary_warped
out_img[:,:,2] = binary_warped
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
def orig_img_lane_locate(warped, image, left_fitx, right_fitx, ploty, leftx_base, rightx_base):
M, inv_M = get_transform_M(warped)
# Create an image to draw the lines on
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, inv_M, (image.shape[1], image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(image, 1, newwarp, 0.3, 0)
l_curv, r_curv = get_curvature(ploty, left_fitx, right_fitx)
offset = 600 - (leftx_base + rightx_base) / 2
xm_per_pix = 3.7/700
pos = float("{0:.2f}".format(offset*xm_per_pix))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(result, str(int(l_curv)) + 'm', (200,100), font, 2,(255,255,255),2)
cv2.putText(result, str(int(r_curv)) + 'm', (1000,100), font, 2,(255,255,255),2)
cv2.putText(result, str(pos) + 'm', (500,100), font, 2,(255,255,255),2)
plt.imshow(result)
return result
In [23]:
binary_warped = warp_binary_imgs[0]
left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base = fit_lane_line(binary_warped, True)
In [24]:
final_visualize(binary_warped, left_fit, right_fit)
In [25]:
im = mpimg.imread('output_images/test1_undistorted.jpg')
res = orig_img_lane_locate(binary_warped, im, left_fitx, right_fitx, ploty, leftx_base, rightx_base)
In [26]:
binary_warped = warp_binary_imgs[1]
left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base = fit_lane_line(binary_warped, True)
In [27]:
final_visualize(binary_warped, left_fit, right_fit)
In [28]:
im = mpimg.imread('output_images/test2_undistorted.jpg')
res = orig_img_lane_locate(binary_warped, im, left_fitx, right_fitx, ploty, leftx_base, rightx_base)
In [29]:
binary_warped = warp_binary_imgs[2]
res = left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base = fit_lane_line(binary_warped, True)
In [30]:
final_visualize(binary_warped, left_fit, right_fit)
In [31]:
im = mpimg.imread('output_images/test3_undistorted.jpg')
res = orig_img_lane_locate(binary_warped, im, left_fitx, right_fitx, ploty, leftx_base, rightx_base)
In [32]:
binary_warped = warp_binary_imgs[3]
left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base = fit_lane_line(binary_warped, True)
In [33]:
final_visualize(binary_warped, left_fit, right_fit)
In [34]:
im = mpimg.imread('output_images/test4_undistorted.jpg')
res = orig_img_lane_locate(binary_warped, im, left_fitx, right_fitx, ploty, leftx_base, rightx_base)
In [35]:
binary_warped = warp_binary_imgs[4]
left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base = fit_lane_line(binary_warped, True)
In [36]:
final_visualize(binary_warped, left_fit, right_fit)
In [37]:
im = mpimg.imread('output_images/test5_undistorted.jpg')
res = orig_img_lane_locate(binary_warped, im, left_fitx, right_fitx, ploty, leftx_base, rightx_base)
In [38]:
binary_warped = warp_binary_imgs[5]
left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base = fit_lane_line(binary_warped, True)
In [39]:
final_visualize(binary_warped, left_fit, right_fit)
In [40]:
im = mpimg.imread('output_images/test6_undistorted.jpg')
res = orig_img_lane_locate(binary_warped, im, left_fitx, right_fitx, ploty, leftx_base, rightx_base)
In [41]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
import cv2
def process_image(img):
global i
warp_img = warp(img)
bin_warp = generate_binary(warp_img, False)
left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base = fit_lane_line(bin_warp, False)
res = orig_img_lane_locate(binary_warped, img, left_fitx, right_fitx, ploty, leftx_base, rightx_base)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(res, 'FRM=' + str(int(i)), (100,150), font, 2,(255,255,255),2)
cv2.imwrite('video_out_images' + '/' + str(i) + '.jpg', res)
i = i + 1
return res
def process_image2(img):
global i
dstRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
i = i + 1
return dstRGB
i = 0
white_output = 'project_output_video.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
# clip1 = VideoFileClip("project_video.mp4").subclip(0,5)
#clip1 = VideoFileClip("project_video.mp4")
#white_clip = clip1.fl_image(process_image2)
#%time white_clip.write_videofile(white_output, audio=False)
In [42]:
img1 = show_warped('video_images/1038.jpg', True)
In [43]:
bin_warp = generate_binary(img1, True)
In [44]:
left_fit, right_fit, ploty, left_fitx, right_fitx, leftx_base, rightx_base = fit_lane_line(bin_warp, True)
In [45]:
im = mpimg.imread('video_images/1038.jpg')
res = orig_img_lane_locate(bin_warp, im, left_fitx, right_fitx, ploty, leftx_base, rightx_base)
In [46]:
res = process_image(im)
plt.imshow(res)
Out[46]:
In [47]:
im = mpimg.imread('video_images/613.jpg')
res = process_image(im)
plt.imshow(res)
Out[47]:
In [48]:
im = mpimg.imread('video_images/578.jpg')
res = process_image(im)
plt.imshow(res)
Out[48]:
In [49]:
im = mpimg.imread('video_images/618.jpg')
res = process_image(im)
plt.imshow(res)
Out[49]:
In [50]:
im = mpimg.imread('video_images/1045.jpg')
res = process_image(im)
plt.imshow(res)
Out[50]:
In [51]:
im = mpimg.imread('video_images/679.jpg')
#bin_im = white_thresh(im)
res = process_image(im)
#plt.imshow(bin_im, cmap='gray')
plt.imshow(res)
Out[51]:
In [52]:
im = mpimg.imread('video_images/630.jpg')
#bin_im = white_thresh(im)
res = process_image(im)
#plt.imshow(bin_im, cmap='gray')
plt.imshow(res)
Out[52]:
In [ ]:
i = 0
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(process_image)
%time white_clip.write_videofile(white_output, audio=False)